Abstract:Ice accretion is a crucial factor affecting the safe and stable operation of wind turbines. Developing a numerical model for simulating ice formation on wind turbine blades is essential for predicting icing phenomena. Although the finite element method is currently the most widely used approach, it is computationally intensive and inefficient for large-scale applications. This study focuses on the blades of a 300 kW wind turbine, employing a profile segmentation method to investigate water droplet impact, freezing, and ice accretion morphology changes on blade surfaces. A multiphase flow simulation model for air and liquid on the blade surface is developed, and formulas for local and overall collision and freezing coefficients are derived. This approach enables characterization of overall water droplet impact and freezing behavior with reduced computational load. Results reveal that the water droplet collision coefficient decreases gradually from the blade tip toward the root, with reductions exceeding 80% in the maximum values of β₁ and α₁ at approximately 0.5R. Maximum water droplet capture occurs near the blade tip (0.8R to 0.9R), while significant ice accretion predominantly occurs between 0.5R and R. The overflow effect of the water film results in low freezing coefficients in the droplet collision zone but higher values in the overflow region. Furthermore, closer to the blade tip, ice growth exhibits greater iterative shape changes, and reduced linearity in the ice accretion rate.

Abstract:Existing conductor icing models primarily rely on four environmental parameters: median volume diameter (MVD) of water droplets, liquid water content, wind speed, and ambient temperature, while giving limited attention to droplet size distribution characteristics. This study adopts the Langmuir droplet size distribution spectrum as a basis to develop finite element and analytical models for simulating water droplet collision characteristics on conductors. It compares the water droplet collision coefficient α₁ calculated using the droplet size distribution spectrum with that obtained using MVD. Additionally, the concept of the characteristic median volume diameter d_x is introduced. Results show that, compared to the finite element method, the analytical method offers significant advantages in terms of simplicity and computational efficiency, with an average error of approximately 0.1. The error Δα₁ between α₁ calculated using MVD and that derived from the size distribution spectrum depends on the distance between MVD and d_x. The closer MVD is to d_x, the smaller the Δα₁ is, and vice versa.

Abstract:Variation in ice thickness with altitude results in a non-uniform icing distribution on conductors. This study defines non-uniform icing and ice-shedding conditions and employs an additional element method to numerically simulate the dynamic response of tower-line systems after ice-shedding from conductors with non-uniform icing. Finite element models of typical isolated and multi-span tower-line coupling systems for 500 kV quad-bundle transmission lines are established, and their dynamic responses are analyzed. The variation patterns of characteristic parameters, including load impact factors, maximum reaction forces at connection points, longitudinal unbalanced tension, and de-icing jump height, with respect to line span, elevation difference ratio, and icing thickness are examined under varying structural, icing, and ice-shedding parameters. The strength of towers under extreme conditions is also analyzed. The obtained results provide critical guidance for the design of transmission tower heads in ice-prone regions.

Abstract:Ultra-high voltage (UHV) long-span (LS) transmission lines, characterized by large tower heights and long spans, are highly susceptible to wind loads. Wind fragility analysis is an essential method to assess their reliability under wind-induced actions. Using an UHV-LS transmission tower in Anhui Province as a case study, this research applies random wind vibration response theory and China’s current overhead transmission line loading specifications to determine the structural response distribution under wind loads. The analysis incorporates uncertainties in structural material properties to establish the probabilistic distribution of wind load-carrying capacity. Performance levels are quantitatively evaluated using tower-top displacement and corrected inter-segment displacement angles as indicators, leading to the development of wind fragility curves. The results indicate that the quasi-static wind effect distribution of the transmission tower-line system can be obtained using probability-based methods for the first time. The fragility assessment shows that performance levels based on tower-top displacement are more conservative than those based on modified inter-segment displacement angles. Additionally, as structural damage intensifies, the influence of material uncertainty on load-bearing capacity becomes more pronounced. Overall, wind fragility analysis shows that UHV towers exhibit good wind reliability under design wind loads, although their wind-induced failure shows brittle characteristics.

Abstract:To clarify the impact of aerodynamic coupling characteristics on the galloping amplitude of crescent-shaped iced single conductor, an analysis model of wind-induced vibration response of the conductor is established based on aerodynamic theory. The fluid-structure coupling method is used to calculate the displacement time history of the conductor, and the influence of aerodynamic coupling characteristics on its galloping amplitude is analyzed. The results show that the frequency ratio and the degree of freedom have little influence on the aerodynamic lift-drag coefficient of the conductor, which shows that the aerodynamic force on the conductor in the flow field does not change with the different degrees of freedom and frequency ratio. In different degrees of freedom systems, the conductor gallops greatly at the angle of attack of 20°. In the vertical single-degree-of-freedom system, the galloping amplitude of the conductor is greatly influenced by the frequency ratio, and the larger the frequency ratio, the smaller the galloping amplitude. In the vertical-horizontal two-degree-of-freedom system, when the vertical frequency is equal to the horizontal frequency, the conductor is coupled to vibrate, and its galloping amplitude in the vertical direction is greater than that in other frequencies. When the conductor gallops in the flow field, its horizontal movement promotes vertical vibration, and its motion trajectory in the flow field is elongated. The research results clarify the influence of aerodynamic coupling characteristics on the galloping of crescent-shaped iced single conductor, which can provide some theoretical reference for the study of galloping and dancing prevention of the conductor in engineering.

Abstract:To address the vulnerability of UAV-based air-to-ground communication links to eavesdropping, this paper proposes a covert information-mapped generalized spatial and direction modulation (CIM-GSDM) system. In this system, information is concealed within the indices of activated receiver subsets and their selection combinations, while artificial noise orthogonal to the legitimate channel is introduced to disrupt potential eavesdroppers. To further enhance transmission security, a joint optimization framework for the precoding matrix and power allocation factor is developed, effectively managing multi-beam transmission and the distribution of artificial noise. The physical layer security metric, based on the system’s secrecy rate, is derived and used as the optimization objective. To solve the resulting non-convex joint optimization problem, alternating optimization of the precoding matrix and power allocations factor is employed. A natural gradient descent method with Nesterov’s acceleration is proposed to efficiently update the precoding matrix, addressing computational complexity due to the large CIM-GSDM symbol candidate set. Furthermore, a suboptimal closed-form solution for the power allocation factor is derived based on maximizing the product of the legitimate user’s signal-to-noise ratio (SNR) and the eavesdropper’s interference-to-signal-plus-noise ratio (ISNR). Simulation results demonstrate that the proposed optimization algorithm significantly reduces the eavesdropper’s interception rate while ensuring the legitimate user’s achievable rate, effectively guaranteeing secure transmission in the CIM-GSDM system. Compared to traditional beamforming algorithms and fixed power allocation methods, the proposed algorithm achieves superior security performance.

Abstract:To address the challenge of autonomous vehicle decision-making and control at unsignalized intersections, this study investigates the merging behavior of two vehicles at a two-way single-lane intersection. Reinforcement learning is used to establish a mapping between the vehicle state space and action space for autonomous decision-making. To overcome the limitations of overly simplified speed settings in existing studies, real-world trajectory data of surrounding vehicles are used to construct an environmental traffic model. The autoregressive moving average (ARMA) model is applied to predict the speeds of surrounding vehicles. By integrating the predicted speed profiles with the autonomous vehicle’s motion parameters, a forward decision-making model is established to calculate reference speeds. These reference speeds are incorporated into the reinforcement learning reward function to accelerate training convergence. Experimental results show that the proposed model achieves rapid convergence, and the trained agent can safely navigate the intersection while interacting with surrounding vehicles exhibiting diverse driving behaviors. This work provides a reference framework for improving the safety and efficiency of autonomous vehicle decision-making at unsignalized intersections.

Abstract:High and steep slopes are common during the construction of large-scale projects, and their deformation often leads to geological hazards, posing significant threats to life and property. Efficiently collecting displacement data and developing an accurate predictive model are therefore essential. This study proposes a Transformer-CNN hybrid model that integrates convolutional layers and residual structures into the Transformer architecture. The optimized model is applied to displacement data obtained from the Beidou satellite system in a large water conservancy project in Chongqing. Experimental results indicate that the Transformer-CNN model achieves lower MAE, MSE, and RMSE values compared to single-model approaches, demonstrating superior prediction accuracy. These findings suggest that the proposed model offers a practical solution for predicting and analyzing slope deformation in similar engineering projects.

Abstract:With the rapid progress of artificial intelligence, the application of natural language processing(NLP) has expanded across various fields, such as finance, medical care, education, and e-commerce, offering efficient and intelligent solutions for diverse business arenas. This study primarily discusses the specific application scenarios, challenges, and potential future developments of ChatGPT in oil and gas exploration and development. Using Python to access the ChatGPT API, illustrative examples demonstrate its strengths in information retrieval, decision support, and customer service within this industry. These advantages translate into improved operational efficiency, optimized decision-making, enhanced customer service and communication, and innovative problem-solving methods. Additionally, ChatGPT’s strong programming capabilities further improve the efficiency of AI applications in this field. Fine-tuning ChatGPT with domain-specific knowledge and data enables the development of dedicated intelligent systems for oil and gas operations, where the quantity and quality of data determine the model's accuracy and expertise. Nevertheless, challenges remain, such as response reliability, data quality, model accuracy, and data security. Future trends are expected to include enhanced comprehension, improved creativity, and greater interactivity. Furthermore, ChatGPT can be integrated with data lakes to support data querying, fault prediction, automated report generation, training, and workflow automation. With continuous upgrades and user-driving optimization, NLP technologies such as ChatGPT are anticipated to play an increasingly critical role in the oil and gas sector.

Abstract:With the rapid advancement of electric power Internet of Things (EPIoT) technology, the development of a secure and efficient energy Internet has become increasingly important. Identification and authentication of electric power terminal devices are fundamental to ensuring the safe and stable operation of the energy Internet. To realize efficient data collection and secure authentication for a large number of terminal devices, this paper proposes an RFID-based authentication scheme for EPIoT. The scheme integrates RFID (radio frequency identification) technology with the national cryptographic algorithms SM3 and SM4, achieving mutual authentication between readers and terminal devices while ensuring secure transmission of power communication data and reducing computational overhead for device tags. Security analysis shows that the proposed scheme satisfies key security requirements, such as untraceability, resistance to replay attacks, de-synchronization attacks, and denial-of-service attacks. Further verification using BAN logic confirms the mutual authentication capability of the scheme, while performance analysis shows advantages in tag computation, storage, communication overhead, and database search efficiency.